{
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import xgboost as xgb\n",
    "import pickle\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.preprocessing import LabelEncoder,LabelBinarizer\n",
    "from sklearn.model_selection  import cross_val_score\n",
    "from sklearn.model_selection import KFold,train_test_split\n",
    "\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('allstate-claims-severity/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-3164a7eb0086>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'log_loss'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'loss'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "train['log_loss']=np.log(train['loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "离散特征Categorical: 116 features\n",
      "Numerical:14 features\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['cat1',\n",
       " 'cat2',\n",
       " 'cat3',\n",
       " 'cat4',\n",
       " 'cat5',\n",
       " 'cat6',\n",
       " 'cat7',\n",
       " 'cat8',\n",
       " 'cat9',\n",
       " 'cat10',\n",
       " 'cat11',\n",
       " 'cat12',\n",
       " 'cat13',\n",
       " 'cat14',\n",
       " 'cat15',\n",
       " 'cat16',\n",
       " 'cat17',\n",
       " 'cat18',\n",
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       " 'cat64',\n",
       " 'cat65',\n",
       " 'cat66',\n",
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       " 'cat68',\n",
       " 'cat69',\n",
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       " 'cat71',\n",
       " 'cat72',\n",
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       " 'cat75',\n",
       " 'cat76',\n",
       " 'cat77',\n",
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       " 'cat80',\n",
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       " 'cat87',\n",
       " 'cat88',\n",
       " 'cat89',\n",
       " 'cat90',\n",
       " 'cat91',\n",
       " 'cat92',\n",
       " 'cat93',\n",
       " 'cat94',\n",
       " 'cat95',\n",
       " 'cat96',\n",
       " 'cat97',\n",
       " 'cat98',\n",
       " 'cat99',\n",
       " 'cat100',\n",
       " 'cat101',\n",
       " 'cat102',\n",
       " 'cat103',\n",
       " 'cat104',\n",
       " 'cat105',\n",
       " 'cat106',\n",
       " 'cat107',\n",
       " 'cat108',\n",
       " 'cat109',\n",
       " 'cat110',\n",
       " 'cat111',\n",
       " 'cat112',\n",
       " 'cat113',\n",
       " 'cat114',\n",
       " 'cat115',\n",
       " 'cat116',\n",
       " 'cont1',\n",
       " 'cont2',\n",
       " 'cont3',\n",
       " 'cont4',\n",
       " 'cont5',\n",
       " 'cont6',\n",
       " 'cont7',\n",
       " 'cont8',\n",
       " 'cont9',\n",
       " 'cont10',\n",
       " 'cont11',\n",
       " 'cont12',\n",
       " 'cont13',\n",
       " 'cont14']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = [x for x in train.columns if x not in ['id','loss','log_loss']]\n",
    "\n",
    "cat_features =[x for x in train.select_dtypes(include = ['object']) if x not in ['id','loss','log_loss']]\n",
    "\n",
    "num_features =[x for x in train.select_dtypes(exclude = ['object']) if x not in ['id','loss','log_loss']]           \n",
    "\n",
    "print('离散特征Categorical: {} features'.format(len(cat_features)))\n",
    "print('Numerical:{} features'.format(len(num_features)))\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Xtrain: (188318, 130)\n",
      "ytrain: (188318,)\n"
     ]
    }
   ],
   "source": [
    "ntrain = train.shape[0]\n",
    "#ntrain = 188318\n",
    "\n",
    "train_x = train[features]\n",
    "train_y = train['log_loss']\n",
    "\n",
    "for c in range(len(cat_features)):\n",
    "    train_x[cat_features[c]] = train_x[cat_features[c]].astype('category').cat.codes\n",
    "print('Xtrain:',train_x.shape)\n",
    "print('ytrain:',train_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.333292</td>\n",
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       "      <td>0.339244</td>\n",
       "      <td>0.503888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>188316</td>\n",
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       "      <td>0.704364</td>\n",
       "      <td>0.562866</td>\n",
       "      <td>0.34987</td>\n",
       "      <td>0.44767</td>\n",
       "      <td>0.53881</td>\n",
       "      <td>0.863052</td>\n",
       "      <td>0.852865</td>\n",
       "      <td>0.654753</td>\n",
       "      <td>0.721707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>188317</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.810511</td>\n",
       "      <td>0.721460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>188318 rows × 130 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        cat1  cat2  cat3  cat4  cat5  cat6  cat7  cat8  cat9  cat10  ...  \\\n",
       "0          0     1     0     1     0     0     0     0     1      0  ...   \n",
       "1          0     1     0     0     0     0     0     0     1      1  ...   \n",
       "2          0     1     0     0     1     0     0     0     1      1  ...   \n",
       "3          1     1     0     1     0     0     0     0     1      0  ...   \n",
       "4          0     1     0     1     0     0     0     0     1      1  ...   \n",
       "...      ...   ...   ...   ...   ...   ...   ...   ...   ...    ...  ...   \n",
       "188313     0     1     0     0     0     0     0     0     1      0  ...   \n",
       "188314     0     0     0     0     0     1     0     0     0      0  ...   \n",
       "188315     0     1     0     0     0     0     0     1     1      0  ...   \n",
       "188316     0     1     0     0     0     0     0     0     1      1  ...   \n",
       "188317     1     0     0     1     0     0     0     0     0      0  ...   \n",
       "\n",
       "           cont5     cont6     cont7    cont8    cont9   cont10    cont11  \\\n",
       "0       0.310061  0.718367  0.335060  0.30260  0.67135  0.83510  0.569745   \n",
       "1       0.885834  0.438917  0.436585  0.60087  0.35127  0.43919  0.338312   \n",
       "2       0.397069  0.289648  0.315545  0.27320  0.26076  0.32446  0.381398   \n",
       "3       0.422268  0.440945  0.391128  0.31796  0.32128  0.44467  0.327915   \n",
       "4       0.704268  0.178193  0.247408  0.24564  0.22089  0.21230  0.204687   \n",
       "...          ...       ...       ...      ...      ...      ...       ...   \n",
       "188313  0.939556  0.242437  0.289949  0.24564  0.30859  0.32935  0.223038   \n",
       "188314  0.704268  0.334270  0.382000  0.63475  0.40455  0.47779  0.307628   \n",
       "188315  0.482436  0.345883  0.370534  0.24564  0.45808  0.47779  0.445614   \n",
       "188316  0.340543  0.704364  0.562866  0.34987  0.44767  0.53881  0.863052   \n",
       "188317  0.281143  0.844563  0.533048  0.97123  0.93383  0.83814  0.932195   \n",
       "\n",
       "          cont12    cont13    cont14  \n",
       "0       0.594646  0.822493  0.714843  \n",
       "1       0.366307  0.611431  0.304496  \n",
       "2       0.373424  0.195709  0.774425  \n",
       "3       0.321570  0.605077  0.602642  \n",
       "4       0.202213  0.246011  0.432606  \n",
       "...          ...       ...       ...  \n",
       "188313  0.220003  0.333292  0.208216  \n",
       "188314  0.301921  0.318646  0.305872  \n",
       "188315  0.443374  0.339244  0.503888  \n",
       "188316  0.852865  0.654753  0.721707  \n",
       "188317  0.946432  0.810511  0.721460  \n",
       "\n",
       "[188318 rows x 130 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple XGBoost Model\n",
    "    首先，我们训练一个基本的xgboost模型，然后进行参数调节通过交叉验证来观察结果的变换，使用平均绝对误差衡量mean_absolute_error(np.exp(y),np.exp(yhat))\n",
    "    xgboost自定义一个数据矩阵类DMatrix，会在训练开始时，进行一边预处理，从而提高之后每次迭代的效率\n",
    "### 结果衡量方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#评估策略，e的次幂，用来评估。\n",
    "#结果衡量方法：使用平均绝对误差来衡量\n",
    "#mean_absolute_error(np.exp(y), np.exp(yhat))。\n",
    "#定义计算损失值的函数\n",
    "def xg_eval_mae(yhat,dtrain):\n",
    "    y = dtrain.get_label()\n",
    "    return 'mae',mean_absolute_error(np.exp(y),np.exp(yhat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xgboost.core.DMatrix at 0x16a3f9c53c8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据类型转换成库可以使用的底层格式。\n",
    "dtrain = xgb.DMatrix(train_x,train['log_loss'])\n",
    "dtrain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Xgboost参数  \n",
    "         \n",
    "    ·'booster':'gbtree',梯度提升树\n",
    "    ·'objective': 'multi:softmax', 多分类的问题  \n",
    "    ·'num_class':10, 类别数，与 multisoftmax 并用\n",
    "    ·'gamma':损失下降多少才进行分裂\n",
    "    ·'max_depth':12, 构建树的深度，越大越容易过拟合\n",
    "    ·'lambda':2, 控制模型复杂度的权重值的L2正则化项参数，参数越大，模型越不容易过拟合。\n",
    "    ·'subsample':0.7, 随机采样训练样本 // 取70%的数据训练\n",
    "    ·'colsample_bytree':0.7, 生成树时进行的列采样\n",
    "    ·'min_child_weight':3, 孩子节点中最小的样本权重和。如果一个叶子节点的样本权重和小于min_child_weight则拆分过程结束\n",
    "    ·'silent':0 ,设置成1则没有运行信息输出，最好是设置为0.\n",
    "    ·'eta': 0.007, 如同学习率。//后加树 的贡献率\n",
    "    ·'seed':1000,\n",
    "    ·'nthread':7, cpu 线程数\n",
    "————————————————  \n",
    "版权声明：本文为CSDN博主「好瘦的小胖子」的原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接及本声明。\n",
    "原文链接：https://blog.csdn.net/qq_29547673/article/details/89319061"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_params = {\n",
    "    'seed': 0,\n",
    "    'eta': 0.1,\n",
    "    'colsample_bytree': 0.5,\n",
    "    'silent': 1,\n",
    "    'subsample': 0.5,\n",
    "    'objective': 'reg:linear',\n",
    "    'max_depth': 5,\n",
    "    'min_child_weight': 3\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用交叉验证 xgb.cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV score: 1220.110026\n",
      "Wall time: 1min 3s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "#feval:评估策略\n",
    "bst_cv1 = xgb.cv(xgb_params, dtrain, num_boost_round=50, nfold=3, seed=0, \n",
    "                feval=xg_eval_mae, maximize=False, early_stopping_rounds=10)\n",
    "\n",
    "print ('CV score:', bst_cv1.iloc[-1,:]['test-mae-mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16a3f9c5b08>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 381
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "bst_cv1[['train-mae-mean', 'test-mae-mean']].plot() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 上面是我们第一个模型\n",
    "    ·没有发生过拟合  \n",
    "    ·只建立了50个树模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost 参数调节\n",
    "    Step 1: 选择一组初始参数\n",
    "    Step 2: 改变 max_depth 和 min_child_weight.\n",
    "    Step 3: 调节 gamma 降低模型过拟合风险.\n",
    "    Step 4: 调节 subsample 和 colsample_bytree 改变数据采样策略.\n",
    "    Step 5: 调节学习率 eta.  \n",
    "    \n",
    "经过xgboost的调参策略。得到更好的参数：  \n",
    "XGBoostRegressor(num_boost_round=200, gamma=0.2, max_depth=8, min_child_weight=6, colsample_bytree=0.6, subsample=0.9, eta=0.07)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class XGBoostRegressor(object):\n",
    "    def __init__(self, **kwargs):\n",
    "        self.params = kwargs\n",
    "        if 'num_boost_round' in self.params:\n",
    "            self.num_boost_round = self.params['num_boost_round']\n",
    "        self.params.update({'silent': 1, 'objective': 'reg:linear', 'seed': 0})#默认参数\n",
    "        \n",
    "    def fit(self, x_train, y_train):\n",
    "        '''\n",
    "        #数据类型转换,#用参数去训练xgboost模型\n",
    "        '''\n",
    "        dtrain = xgb.DMatrix(x_train, y_train) \n",
    "        self.bst = xgb.train(params=self.params, dtrain=dtrain, num_boost_round=self.num_boost_round,\n",
    "                             feval=xg_eval_mae, maximize=False)\n",
    "        \n",
    "    def predict(self, x_pred):\n",
    "        dpred = xgb.DMatrix(x_pred)\n",
    "        self.bst = xgb.train(params=self.params, dtrain=dtrain, num_boost_round=self.num_boost_round,\n",
    "                             feval=xg_eval_mae, maximize=False)\n",
    "        return self.bst.predict(dpred)\n",
    "    \n",
    "    def kfold(self, x_train, y_train, nfold=5):\n",
    "        dtrain = xgb.DMatrix(x_train, y_train)\n",
    "        cv_rounds = xgb.cv(params=self.params, dtrain=dtrain, num_boost_round=self.num_boost_round,\n",
    "                           nfold=nfold, feval=xg_eval_mae, maximize=False, early_stopping_rounds=10)\n",
    "        return cv_rounds.iloc[-1,:]\n",
    "    \n",
    "    def plot_feature_importances(self):\n",
    "        feat_imp = pd.Series(self.bst.get_fscore()).sort_values(ascending=False)\n",
    "        feat_imp.plot(title='Feature Importances')\n",
    "        plt.ylabel('Feature Importance Score')\n",
    "        \n",
    "    def get_params(self, deep=True):\n",
    "        return self.params\n",
    " \n",
    "    def set_params(self, **params):\n",
    "        self.params.update(params)\n",
    "        return self \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#衡量标准\n",
    "def mae_score(y_true, y_pred):\n",
    "    return mean_absolute_error(np.exp(y_true), np.exp(y_pred))\n",
    "\n",
    "mae_scorer = make_scorer(mae_score, greater_is_better=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "bst = XGBoostRegressor(eta=0.1, colsample_bytree=0.5, subsample=0.5, \n",
    "                       max_depth=5, min_child_weight=3, num_boost_round=50)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "train-rmse-mean       0.558938\n",
       "train-rmse-std        0.001005\n",
       "test-rmse-mean        0.562665\n",
       "test-rmse-std         0.002445\n",
       "train-mae-mean     1209.707324\n",
       "train-mae-std         3.004207\n",
       "test-mae-mean      1218.884204\n",
       "test-mae-std          8.982969\n",
       "Name: 49, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bst.kfold(train_x, train_y, nfold=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 按照训练集处理方式，处理我们的测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>...</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
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       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.836443</td>\n",
       "      <td>0.482425</td>\n",
       "      <td>0.443760</td>\n",
       "      <td>0.71330</td>\n",
       "      <td>0.51890</td>\n",
       "      <td>0.60401</td>\n",
       "      <td>0.689039</td>\n",
       "      <td>0.675759</td>\n",
       "      <td>0.453468</td>\n",
       "      <td>0.208045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.718531</td>\n",
       "      <td>0.212308</td>\n",
       "      <td>0.325779</td>\n",
       "      <td>0.29758</td>\n",
       "      <td>0.34365</td>\n",
       "      <td>0.30529</td>\n",
       "      <td>0.245410</td>\n",
       "      <td>0.241676</td>\n",
       "      <td>0.258586</td>\n",
       "      <td>0.297232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>0.397069</td>\n",
       "      <td>0.369930</td>\n",
       "      <td>0.342355</td>\n",
       "      <td>0.40028</td>\n",
       "      <td>0.33237</td>\n",
       "      <td>0.31480</td>\n",
       "      <td>0.348867</td>\n",
       "      <td>0.341872</td>\n",
       "      <td>0.592264</td>\n",
       "      <td>0.555955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>0.302678</td>\n",
       "      <td>0.398862</td>\n",
       "      <td>0.391833</td>\n",
       "      <td>0.23688</td>\n",
       "      <td>0.43731</td>\n",
       "      <td>0.50556</td>\n",
       "      <td>0.359572</td>\n",
       "      <td>0.352251</td>\n",
       "      <td>0.301535</td>\n",
       "      <td>0.825823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125541</td>\n",
       "      <td>587617</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>0.281143</td>\n",
       "      <td>0.438917</td>\n",
       "      <td>0.815941</td>\n",
       "      <td>0.39455</td>\n",
       "      <td>0.48740</td>\n",
       "      <td>0.40666</td>\n",
       "      <td>0.550529</td>\n",
       "      <td>0.538473</td>\n",
       "      <td>0.298734</td>\n",
       "      <td>0.345946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125542</td>\n",
       "      <td>587621</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>0.674529</td>\n",
       "      <td>0.346948</td>\n",
       "      <td>0.424968</td>\n",
       "      <td>0.47669</td>\n",
       "      <td>0.25753</td>\n",
       "      <td>0.26894</td>\n",
       "      <td>0.324486</td>\n",
       "      <td>0.352251</td>\n",
       "      <td>0.490001</td>\n",
       "      <td>0.290576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125543</td>\n",
       "      <td>587627</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.794794</td>\n",
       "      <td>0.808958</td>\n",
       "      <td>0.511502</td>\n",
       "      <td>0.72299</td>\n",
       "      <td>0.94438</td>\n",
       "      <td>0.83510</td>\n",
       "      <td>0.933174</td>\n",
       "      <td>0.926619</td>\n",
       "      <td>0.848129</td>\n",
       "      <td>0.808125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125544</td>\n",
       "      <td>587629</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>0.302678</td>\n",
       "      <td>0.372125</td>\n",
       "      <td>0.388545</td>\n",
       "      <td>0.31796</td>\n",
       "      <td>0.32128</td>\n",
       "      <td>0.36974</td>\n",
       "      <td>0.307628</td>\n",
       "      <td>0.301921</td>\n",
       "      <td>0.608259</td>\n",
       "      <td>0.361542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125545</td>\n",
       "      <td>587634</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.413817</td>\n",
       "      <td>0.221699</td>\n",
       "      <td>0.242044</td>\n",
       "      <td>0.25461</td>\n",
       "      <td>0.31399</td>\n",
       "      <td>0.25183</td>\n",
       "      <td>0.245410</td>\n",
       "      <td>0.241676</td>\n",
       "      <td>0.287682</td>\n",
       "      <td>0.220323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125546 rows × 131 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id cat1 cat2 cat3 cat4 cat5 cat6 cat7 cat8 cat9  ...     cont5  \\\n",
       "0            4    A    B    A    A    A    A    A    A    B  ...  0.281143   \n",
       "1            6    A    B    A    B    A    A    A    A    B  ...  0.836443   \n",
       "2            9    A    B    A    B    B    A    B    A    B  ...  0.718531   \n",
       "3           12    A    A    A    A    B    A    A    A    A  ...  0.397069   \n",
       "4           15    B    A    A    A    A    B    A    A    A  ...  0.302678   \n",
       "...        ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...       ...   \n",
       "125541  587617    A    A    A    B    A    A    A    A    A  ...  0.281143   \n",
       "125542  587621    A    A    A    A    B    B    A    B    A  ...  0.674529   \n",
       "125543  587627    B    B    A    A    B    A    A    A    B  ...  0.794794   \n",
       "125544  587629    A    A    A    A    A    B    A    B    A  ...  0.302678   \n",
       "125545  587634    A    B    A    A    A    A    A    A    B  ...  0.413817   \n",
       "\n",
       "           cont6     cont7    cont8    cont9   cont10    cont11    cont12  \\\n",
       "0       0.466591  0.317681  0.61229  0.34365  0.38016  0.377724  0.369858   \n",
       "1       0.482425  0.443760  0.71330  0.51890  0.60401  0.689039  0.675759   \n",
       "2       0.212308  0.325779  0.29758  0.34365  0.30529  0.245410  0.241676   \n",
       "3       0.369930  0.342355  0.40028  0.33237  0.31480  0.348867  0.341872   \n",
       "4       0.398862  0.391833  0.23688  0.43731  0.50556  0.359572  0.352251   \n",
       "...          ...       ...      ...      ...      ...       ...       ...   \n",
       "125541  0.438917  0.815941  0.39455  0.48740  0.40666  0.550529  0.538473   \n",
       "125542  0.346948  0.424968  0.47669  0.25753  0.26894  0.324486  0.352251   \n",
       "125543  0.808958  0.511502  0.72299  0.94438  0.83510  0.933174  0.926619   \n",
       "125544  0.372125  0.388545  0.31796  0.32128  0.36974  0.307628  0.301921   \n",
       "125545  0.221699  0.242044  0.25461  0.31399  0.25183  0.245410  0.241676   \n",
       "\n",
       "          cont13    cont14  \n",
       "0       0.704052  0.392562  \n",
       "1       0.453468  0.208045  \n",
       "2       0.258586  0.297232  \n",
       "3       0.592264  0.555955  \n",
       "4       0.301535  0.825823  \n",
       "...          ...       ...  \n",
       "125541  0.298734  0.345946  \n",
       "125542  0.490001  0.290576  \n",
       "125543  0.848129  0.808125  \n",
       "125544  0.608259  0.361542  \n",
       "125545  0.287682  0.220323  \n",
       "\n",
       "[125546 rows x 131 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('allstate-claims-severity/test.csv')\n",
    "\n",
    "test#没有loss列,loss需要预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cat1</th>\n",
       "      <th>cat2</th>\n",
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       "      <th>cat10</th>\n",
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       "      <th>cont10</th>\n",
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       "      <th>cont12</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0.482425</td>\n",
       "      <td>0.443760</td>\n",
       "      <td>0.71330</td>\n",
       "      <td>0.51890</td>\n",
       "      <td>0.60401</td>\n",
       "      <td>0.689039</td>\n",
       "      <td>0.675759</td>\n",
       "      <td>0.453468</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
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       "      <td>0.718531</td>\n",
       "      <td>0.212308</td>\n",
       "      <td>0.325779</td>\n",
       "      <td>0.29758</td>\n",
       "      <td>0.34365</td>\n",
       "      <td>0.30529</td>\n",
       "      <td>0.245410</td>\n",
       "      <td>0.241676</td>\n",
       "      <td>0.258586</td>\n",
       "      <td>0.297232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.397069</td>\n",
       "      <td>0.369930</td>\n",
       "      <td>0.342355</td>\n",
       "      <td>0.40028</td>\n",
       "      <td>0.33237</td>\n",
       "      <td>0.31480</td>\n",
       "      <td>0.348867</td>\n",
       "      <td>0.341872</td>\n",
       "      <td>0.592264</td>\n",
       "      <td>0.555955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.302678</td>\n",
       "      <td>0.398862</td>\n",
       "      <td>0.391833</td>\n",
       "      <td>0.23688</td>\n",
       "      <td>0.43731</td>\n",
       "      <td>0.50556</td>\n",
       "      <td>0.359572</td>\n",
       "      <td>0.352251</td>\n",
       "      <td>0.301535</td>\n",
       "      <td>0.825823</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 130 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   cat1  cat2  cat3  cat4  cat5  cat6  cat7  cat8  cat9  cat10  ...     cont5  \\\n",
       "0     0     1     0     0     0     0     0     0     1      0  ...  0.281143   \n",
       "1     0     1     0     1     0     0     0     0     1      0  ...  0.836443   \n",
       "2     0     1     0     1     1     0     1     0     1      1  ...  0.718531   \n",
       "3     0     0     0     0     1     0     0     0     0      0  ...  0.397069   \n",
       "4     1     0     0     0     0     1     0     0     0      0  ...  0.302678   \n",
       "\n",
       "      cont6     cont7    cont8    cont9   cont10    cont11    cont12  \\\n",
       "0  0.466591  0.317681  0.61229  0.34365  0.38016  0.377724  0.369858   \n",
       "1  0.482425  0.443760  0.71330  0.51890  0.60401  0.689039  0.675759   \n",
       "2  0.212308  0.325779  0.29758  0.34365  0.30529  0.245410  0.241676   \n",
       "3  0.369930  0.342355  0.40028  0.33237  0.31480  0.348867  0.341872   \n",
       "4  0.398862  0.391833  0.23688  0.43731  0.50556  0.359572  0.352251   \n",
       "\n",
       "     cont13    cont14  \n",
       "0  0.704052  0.392562  \n",
       "1  0.453468  0.208045  \n",
       "2  0.258586  0.297232  \n",
       "3  0.592264  0.555955  \n",
       "4  0.301535  0.825823  \n",
       "\n",
       "[5 rows x 130 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#features_test = [x for x in test.columns if x not in ['id']]\n",
    "\n",
    "test_x = test[features]\n",
    "\n",
    "#将类别数据的类别用数字替换\n",
    "for c in range(len(cat_features)):\n",
    "\n",
    "    test_x[cat_features[c]] = test_x[cat_features[c]].astype('category').cat.codes\n",
    "\n",
    "test_x.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据类型转换成库可以使用的底层格式。\n",
    "#dtest_x = xgb.DMatrix(test_x)\n",
    "#dtest_x\n",
    "#得到我们想要的测试集\n",
    "\n",
    "\n",
    "#预测命令：\n",
    "\n",
    "test_y = bst.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7.450635, 125546)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_y[1],len(test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(125546,)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "#math.exp(test_y[0])\n",
    "def test_exp_y_predict(test_y):\n",
    "    '''\n",
    "    取log()的反函数\n",
    "    '''\n",
    "    test_exp_y= np.zeros(len(test_y))\n",
    "    for i in range(len(test_y)):\n",
    "        test_exp_y[i] = math.exp(test_y[i])\n",
    "    return test_exp_y\n",
    "\n",
    "pre_y = test_exp_y_predict(test_y)\n",
    "pre_y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 下面使用经过调参优化后的xgboost模型，进行预测分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = XGBoostRegressor(num_boost_round=200, gamma=0.2, max_depth=8, min_child_weight=6, colsample_bytree=0.6, subsample=0.9, eta=0.07)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 6min 3s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "train-rmse-mean       0.496316\n",
       "train-rmse-std        0.000353\n",
       "test-rmse-mean        0.537659\n",
       "test-rmse-std         0.002246\n",
       "train-mae-mean     1041.517920\n",
       "train-mae-std         1.356585\n",
       "test-mae-mean      1145.610254\n",
       "test-mae-std          8.666762\n",
       "Name: 199, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "best_model.kfold(train_x, train_y, nfold=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def output_predict(test_pre):\n",
    "    '''\n",
    "    处理模型的预测值。\n",
    "    过程：取log()的反函数,输出list格式、保留两位小数的预测值\n",
    "    input:模型直接输出的预测值\n",
    "    output:经过还原的预测值test_pre_y\n",
    "    '''\n",
    "    \n",
    "    #取log()的反函数\n",
    "    test_pre_array= np.zeros(len(test_pre))\n",
    "    for i in range(len(test_pre)):\n",
    "        test_pre_array[i] = math.exp(test_pre[i])\n",
    "    print('test_exp_y.shape:',test_pre_array.shape)\n",
    "    #数组array 转 列表list 格式\n",
    "    test_pre_list = test_pre_array.tolist()\n",
    "    #保留两位小数\n",
    "    test_pre_list = np.around(test_pre_list, decimals=2)\n",
    "    \n",
    "    return test_pre_list\n",
    "\n",
    "\n",
    "def plt_id_loss(test):\n",
    "    plt.figure(figsize=(16,8))\n",
    "    plt.plot(test['id'],test['loss'])\n",
    "    print('test[\\'id\\']个数:',len(test['id']))\n",
    "    plt.title('Loss values per id')\n",
    "    plt.xlabel('id')\n",
    "    plt.ylabel('loss')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    return '数据集id 与 loss 图像绘制成功！'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pre = best_model.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_pre.shape: (125546,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1553.88363867, 1674.73612157, 9128.16204738, ..., 2369.43253642,\n",
       "       1044.38334379, 3081.44380484])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_pre_array=test_exp_y_predict(test_pre)\n",
    "print('test_pre.shape:',test_pre_array.shape)\n",
    "test_pre_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面是我们用xgboost模型预测的test数据集的loss。我们画图来看看预测结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>cat1</th>\n",
       "      <th>cat2</th>\n",
       "      <th>cat3</th>\n",
       "      <th>cat4</th>\n",
       "      <th>cat5</th>\n",
       "      <th>cat6</th>\n",
       "      <th>cat7</th>\n",
       "      <th>cat8</th>\n",
       "      <th>cat9</th>\n",
       "      <th>...</th>\n",
       "      <th>cont6</th>\n",
       "      <th>cont7</th>\n",
       "      <th>cont8</th>\n",
       "      <th>cont9</th>\n",
       "      <th>cont10</th>\n",
       "      <th>cont11</th>\n",
       "      <th>cont12</th>\n",
       "      <th>cont13</th>\n",
       "      <th>cont14</th>\n",
       "      <th>loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.466591</td>\n",
       "      <td>0.317681</td>\n",
       "      <td>0.61229</td>\n",
       "      <td>0.34365</td>\n",
       "      <td>0.38016</td>\n",
       "      <td>0.377724</td>\n",
       "      <td>0.369858</td>\n",
       "      <td>0.704052</td>\n",
       "      <td>0.392562</td>\n",
       "      <td>1553.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.482425</td>\n",
       "      <td>0.443760</td>\n",
       "      <td>0.71330</td>\n",
       "      <td>0.51890</td>\n",
       "      <td>0.60401</td>\n",
       "      <td>0.689039</td>\n",
       "      <td>0.675759</td>\n",
       "      <td>0.453468</td>\n",
       "      <td>0.208045</td>\n",
       "      <td>1674.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.212308</td>\n",
       "      <td>0.325779</td>\n",
       "      <td>0.29758</td>\n",
       "      <td>0.34365</td>\n",
       "      <td>0.30529</td>\n",
       "      <td>0.245410</td>\n",
       "      <td>0.241676</td>\n",
       "      <td>0.258586</td>\n",
       "      <td>0.297232</td>\n",
       "      <td>9128.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>0.369930</td>\n",
       "      <td>0.342355</td>\n",
       "      <td>0.40028</td>\n",
       "      <td>0.33237</td>\n",
       "      <td>0.31480</td>\n",
       "      <td>0.348867</td>\n",
       "      <td>0.341872</td>\n",
       "      <td>0.592264</td>\n",
       "      <td>0.555955</td>\n",
       "      <td>5581.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>0.398862</td>\n",
       "      <td>0.391833</td>\n",
       "      <td>0.23688</td>\n",
       "      <td>0.43731</td>\n",
       "      <td>0.50556</td>\n",
       "      <td>0.359572</td>\n",
       "      <td>0.352251</td>\n",
       "      <td>0.301535</td>\n",
       "      <td>0.825823</td>\n",
       "      <td>780.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125541</td>\n",
       "      <td>587617</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>0.438917</td>\n",
       "      <td>0.815941</td>\n",
       "      <td>0.39455</td>\n",
       "      <td>0.48740</td>\n",
       "      <td>0.40666</td>\n",
       "      <td>0.550529</td>\n",
       "      <td>0.538473</td>\n",
       "      <td>0.298734</td>\n",
       "      <td>0.345946</td>\n",
       "      <td>2027.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125542</td>\n",
       "      <td>587621</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>0.346948</td>\n",
       "      <td>0.424968</td>\n",
       "      <td>0.47669</td>\n",
       "      <td>0.25753</td>\n",
       "      <td>0.26894</td>\n",
       "      <td>0.324486</td>\n",
       "      <td>0.352251</td>\n",
       "      <td>0.490001</td>\n",
       "      <td>0.290576</td>\n",
       "      <td>1973.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125543</td>\n",
       "      <td>587627</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.808958</td>\n",
       "      <td>0.511502</td>\n",
       "      <td>0.72299</td>\n",
       "      <td>0.94438</td>\n",
       "      <td>0.83510</td>\n",
       "      <td>0.933174</td>\n",
       "      <td>0.926619</td>\n",
       "      <td>0.848129</td>\n",
       "      <td>0.808125</td>\n",
       "      <td>2369.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125544</td>\n",
       "      <td>587629</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>...</td>\n",
       "      <td>0.372125</td>\n",
       "      <td>0.388545</td>\n",
       "      <td>0.31796</td>\n",
       "      <td>0.32128</td>\n",
       "      <td>0.36974</td>\n",
       "      <td>0.307628</td>\n",
       "      <td>0.301921</td>\n",
       "      <td>0.608259</td>\n",
       "      <td>0.361542</td>\n",
       "      <td>1044.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125545</td>\n",
       "      <td>587634</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.221699</td>\n",
       "      <td>0.242044</td>\n",
       "      <td>0.25461</td>\n",
       "      <td>0.31399</td>\n",
       "      <td>0.25183</td>\n",
       "      <td>0.245410</td>\n",
       "      <td>0.241676</td>\n",
       "      <td>0.287682</td>\n",
       "      <td>0.220323</td>\n",
       "      <td>3081.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125546 rows × 132 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id cat1 cat2 cat3 cat4 cat5 cat6 cat7 cat8 cat9  ...     cont6  \\\n",
       "0            4    A    B    A    A    A    A    A    A    B  ...  0.466591   \n",
       "1            6    A    B    A    B    A    A    A    A    B  ...  0.482425   \n",
       "2            9    A    B    A    B    B    A    B    A    B  ...  0.212308   \n",
       "3           12    A    A    A    A    B    A    A    A    A  ...  0.369930   \n",
       "4           15    B    A    A    A    A    B    A    A    A  ...  0.398862   \n",
       "...        ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...       ...   \n",
       "125541  587617    A    A    A    B    A    A    A    A    A  ...  0.438917   \n",
       "125542  587621    A    A    A    A    B    B    A    B    A  ...  0.346948   \n",
       "125543  587627    B    B    A    A    B    A    A    A    B  ...  0.808958   \n",
       "125544  587629    A    A    A    A    A    B    A    B    A  ...  0.372125   \n",
       "125545  587634    A    B    A    A    A    A    A    A    B  ...  0.221699   \n",
       "\n",
       "           cont7    cont8    cont9   cont10    cont11    cont12    cont13  \\\n",
       "0       0.317681  0.61229  0.34365  0.38016  0.377724  0.369858  0.704052   \n",
       "1       0.443760  0.71330  0.51890  0.60401  0.689039  0.675759  0.453468   \n",
       "2       0.325779  0.29758  0.34365  0.30529  0.245410  0.241676  0.258586   \n",
       "3       0.342355  0.40028  0.33237  0.31480  0.348867  0.341872  0.592264   \n",
       "4       0.391833  0.23688  0.43731  0.50556  0.359572  0.352251  0.301535   \n",
       "...          ...      ...      ...      ...       ...       ...       ...   \n",
       "125541  0.815941  0.39455  0.48740  0.40666  0.550529  0.538473  0.298734   \n",
       "125542  0.424968  0.47669  0.25753  0.26894  0.324486  0.352251  0.490001   \n",
       "125543  0.511502  0.72299  0.94438  0.83510  0.933174  0.926619  0.848129   \n",
       "125544  0.388545  0.31796  0.32128  0.36974  0.307628  0.301921  0.608259   \n",
       "125545  0.242044  0.25461  0.31399  0.25183  0.245410  0.241676  0.287682   \n",
       "\n",
       "          cont14     loss  \n",
       "0       0.392562  1553.88  \n",
       "1       0.208045  1674.74  \n",
       "2       0.297232  9128.16  \n",
       "3       0.555955  5581.20  \n",
       "4       0.825823   780.43  \n",
       "...          ...      ...  \n",
       "125541  0.345946  2027.97  \n",
       "125542  0.290576  1973.99  \n",
       "125543  0.808125  2369.43  \n",
       "125544  0.361542  1044.38  \n",
       "125545  0.220323  3081.44  \n",
       "\n",
       "[125546 rows x 132 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#数组array 转 列表list 格式\n",
    "test_pre_list = test_pre_array.tolist()\n",
    "\n",
    "#保留两位小数\n",
    "test_pre_list = np.around(test_pre_list, decimals=2)\n",
    "\n",
    "test['loss'] = test_pre_list\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test['id']个数: 125546\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 494,
       "width": 959
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(test['id'],test['loss'])\n",
    "print('test[\\'id\\']个数:',len(test['id']))\n",
    "plt.title('Loss values per id')\n",
    "plt.xlabel('id')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
